Positioning or indoor localization is an essential function of a smart environment, which enables discovering valuable knowledge about the performances, behaviour and preferences of residents, especially those who need long-term monitoring or care. Moreover, location-based applications that utilize such information can offer customizable services according to the dynamics of their users' surroundings. Surveillance and security, health and sleep monitoring, assisted living for elderly people or patients with disabilities and entertainment are a few examples of applications wherein indoor location-aware computing has significantly improved performance.
Generally, there are two different categories of indoor localization systems based on how their sensing infrastructure interacts with the target: device-based and device-free. Most approaches within the prior art exploit device-based systems, where the location of a moving target or human body within the space is determined and represented by a device associated with the moving target or human user such as a Wireless enabled smart phone or a radio-frequency identification (RFID) tag.
These technologies are usually accurate and reliable, but most of them suffer from practical issues such as privacy concerns, physical contact with sensors, high implementation and maintenance cost, and cooperation from the subjects. Conversely, device-free passive (DFP) approaches do not require users to carry any devices or actively participate in the positioning process. Most of the DFP localization systems adopt a radio frequency (RF) sensing infrastructure (such as RFID, microwave, FM signals, etc.) and rely on the influence of the human body's presence and movement to influence these signals, e.g. through reflection.
A few existing systems have employed information gleaned from Wireless signals such as channel state information (CSI) and received signal strength indicator (RSSI) to perform active or passive localization indoors. These systems are mainly enabled by recent wireless technology improvements and the fact that wireless signals are pervasive at most of indoor spaces such as residential, industrial, and public places. The basic idea amongst such systems is to take advantage of these wireless signals to monitor and quantify the distortions arising in the strength and patterns of signals between two nodes of communication (transmitter and receiver) and characterize the environment including human movements and their locations. See, for example, Xiao et al. in “Passive Device-Free Indoor Localization using Channel State Information” (Proc. IEEE 33rd Int. Conf. Distributed Computing Systems (ICDCS), pp. 236-245, 2013) wherein a CSI-based localization system utilizes multiple pairs of transmitter-receiver devices to estimate the location of a moving entity within a sensing area.
Despite some preliminary success, most of these device-free passive systems have been implemented and evaluated using several devices in controlled sensing environments, such as a university laboratory or a classroom, with a large volume of human annotated data and within predefined and short-term scenarios.
On the other hand, wireless signal components are sensitive to many internal and external factors including but not limited to multi-path interference, building attenuation, device and/or antenna orientation issues, changes in the environment (such as changing the position of objects) and signal interference. Therefore, performance of such localization systems usually degrades under realistic conditions and/or over time.
Accordingly, it would be beneficial to provide a system that offers a robust and passive solution for inferring the location of a moving target within an indoor sensing area, which can be created by (at least) a pair of off-the-shelf wireless devices. Furthermore, it would be beneficial for the system to exploit a semi-supervised learning framework employing multiple machine learning techniques in order for the system to maintain long-term accuracy and performances.